About Non-Genomics Precision Health Scan
This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. The scan focus on various conditions including, birth defects, newborn screening, reproductive health, childhood diseases, cancer, chronic diseases, medication, family health history, guidelines and recommendations. The sweep also includes news, reviews, commentaries, tools and database. View Data Selection Criteria
Non-Genomics Precision Health Scan
Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals.
Koh Joel E W et al. Computers in biology and medicine 2021 140105120
Newborn Eye Screening as an Application of AI.
Kumm Jochen et al. Ophthalmic surgery, lasers & imaging retina 2021 52(S2) S17-S22
Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Shi Hui et al. Clinical nutrition (Edinburgh, Scotland) 2021 41(1) 202-210
Value of Radiomics of Perinephric Fat for Prediction of Intraoperative Complexity in Renal Tumor Surgery.
Mühlbauer Julia et al. Urologia internationalis 2021 1-12
Radiomics in surgical oncology: applications and challenges.
Williams Travis L et al. Computer assisted surgery (Abingdon, England) 2021 26(1) 85-96
Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.
Chen Xuguang et al. Neuro-oncology advances 2021 3(1) vdab150
Prediction of locations in medical images using orthogonal neural networks.
Kim Jong Soo et al. European journal of radiology open 2021 8100388
Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter, diagnostic study (with videos).
He Xinqi et al. Gastrointestinal endoscopy 2021
Advancing Traditional Prostate-specific Antigen Kinetics in the Detection of Prostate Cancer: A Machine Learning Model.
Perera Marlon et al. European urology focus 2021
Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).
Lu Zihua et al. Gastrointestinal endoscopy 2021
Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.
Liu Qiu-Ping et al. Abdominal radiology (New York) 2021
Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation.
Romero-Martín Sara et al. Radiology 2021 211590
Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.
Le Page Anne Laure et al. Scientific reports 2021 11(1) 23912
Radiotherapy Standardisation and Artificial Intelligence within the National Cancer Institute's Clinical Trials Network.
Lee S H et al. Clinical oncology (Royal College of Radiologists (Great Britain)) 2021
Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon.
Baleydier Inès et al. PloS one 2021 16(12) e0260776
Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.
Ramesh Siddhi et al. JCO clinical cancer informatics 2021 51208-1219
Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach.
Wang Yueye et al. Frontiers in medicine 2021 8740987
Extended Application of Digital Clock Drawing Test in the Evaluation of Alzheimer's Disease Based on Artificial intelligence and the Neural Basis.
Zheng Xiaoran et al. Current Alzheimer research 2021
Digital approaches to automated and machine learning assessments of hearing: a scoping review.
Wasmann Jan-Wilem et al. Journal of medical Internet research 2021
Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis.
Nelson Charlotte A et al. Journal of the American Medical Informatics Association : JAMIA 2021
Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients.
James Charlotte et al. JAMA network open 2021 4(12) e2136553
Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease.
Shibata Haruto et al. Parkinsonism & related disorders 2021 94104-110
Training method and system for stress management and mental health care of managers based on deep learning.
Liu Mengfan et al. Mathematical biosciences and engineering : MBE 2021 19(1) 371-393
Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians.
Tran Anh Quynh et al. Frontiers in public health 2021 9755644
Application of Deep Learning Technology in Predicting the Risk of Inpatient Death in Intensive Care Unit.
Li Ming et al. Journal of healthcare engineering 2021 20216169481
Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.
Charow Rebecca et al. JMIR medical education 2021 7(4) e31043
Artificial intelligence with deep learning in nuclear medicine and radiology.
Decuyper Milan et al. EJNMMI physics 2021 8(1) 81
Detection of self-harm and suicidal ideation in emergency department triage notes.
Rozova Vlada et al. Journal of the American Medical Informatics Association : JAMIA 2021
A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss.
Oh Yoo Jung et al. The international journal of behavioral nutrition and physical activity 2021 18(1) 160
Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm.
Chen Shu-Chin et al. Omega 2021 302228211060596
Nursing Informatics Research Trends: Findings from an International Survey.
Peltonen Laura-Maria et al. Studies in health technology and informatics 2021 284344-349
AI in Healthcare.
Koski Eileen et al. Studies in health technology and informatics 2021 284295-299
Designing Depression Screening Chatbots.
Giunti G et al. Studies in health technology and informatics 2021 284259-263
Machine learning in systematic reviews: comparing automated text clustering with Lingo3G and human researcher categorization in a rapid review.
Muller Ashley Elizabeth et al. Research synthesis methods 2021
Health chatbots acceptability moderated by perceived stigma and severity: A cross-sectional survey.
Miles Oliver et al. Digital health 2021 720552076211063012
A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.
Zhao Yuzhuo et al. Shock (Augusta, Ga.) 2021 57(1) 48-56
Enablers and Barriers to Deployment of Smartphone-Based Home Vision Monitoring in Clinical Practice Settings.
Korot Edward et al. JAMA ophthalmology 2021
Machine learning-based patient classification system for adults with stroke: A systematic review.
Ruksakulpiwat Suebsarn et al. Chronic illness 2021 17423953211067435
Contemporary Clinical and Coronary Anatomic Risk Model for 30-Day Mortality After Percutaneous Coronary Intervention.
Doll Jacob A et al. Circulation. Cardiovascular interventions 2021 CIRCINTERVENTIONS121010863
Survival prediction among heart patients using machine learning techniques.
Almazroi Abdulwahab Ali et al. Mathematical biosciences and engineering : MBE 2021 19(1) 134-145
Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning.
Peng Junfeng et al. BMC medical informatics and decision making 2021 21(1) 348
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks.
Malik Junaid et al. IEEE transactions on bio-medical engineering 2021 PP
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.
Barbieri Sebastiano et al. International journal of epidemiology 2021
Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease.
Hong Lingzhi et al. Contrast media & molecular imaging 2021 20216423398
Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.
Yan Melissa Y et al. Journal of the American Medical Informatics Association : JAMIA 2021
Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach.
Jeon Kibum et al. BMC infectious diseases 2021 21(1) 1258
Global health systems' data science approach for precision diagnosis of sepsis in early life.
Iregbu Kenneth et al. The Lancet. Infectious diseases 2021
Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.
Codlin Andrew J et al. Scientific reports 2021 11(1) 23895
An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.
Huang Bo et al. Reproductive biology and endocrinology : RB&E 2021 19(1) 185
Disclaimer: Articles listed in Non-Genomics Precision Health Scan are selected by the CDC Office of Genomics and Precision Public Health to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:Mar 28, 2024
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